Gene-Specific Function Prediction for Non-Synonymous Mutations in Monogenic Diabetes Genes

Quan, Li; Liu, Xiaoming; Gibbs, Richard A.; Boerwinkle, Eric; Polychronakos, Constantin; Qu, Hui

doi:10.1371/journal.pone.0104452

Cited by 26 publications

(27 citation statements)

References 53 publications

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“…This support vector machine (SVM)-based ensemble prediction score incorporates ten other scores (SIFT, PolyPhen-2 HDIV, PolyPhen-2 HVAR, GERP++, MutationTaster, Mutation Assessor, FATHMM, LRT, SiPhy, PhyloP) and the maximum allele frequency observed in the 1000 Genomes populations [ 11 ]. In comparison studies [ 26 ], this method was shown to outperform other prediction algorithms with the highest Mathews correlation coefficient (0.474) and relatively low false negative rate (5 %) and false positive rate (57 %). RadialSVM was applied to all rare variants regardless of their classification in HGMD or ClinVar.…”

Section: Methodsmentioning

confidence: 99%

Secondary findings and carrier test frequencies in a large multiethnic sample

et al. 2015

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BackgroundBesides its growing importance in clinical diagnostics and understanding the genetic basis of Mendelian and complex diseases, whole exome sequencing (WES) is a rich source of additional information of potential clinical utility for physicians, patients and their families. We analyzed the frequency and nature of single nucleotide variants (SNVs) considered secondary findings and recessive disease allele carrier status in the exomes of 8554 individuals from a large, randomly sampled cohort study and 2514 patients from a study of presumed Mendelian disease having undergone WES.MethodsWe used the same sequencing platform and data processing pipeline to analyze all samples and characterized the distributions of reported pathogenic (ClinVar, Human Gene Mutation Database (HGMD)) and predicted deleterious variants in the pre-specified American College of Medical Genetics and Genomics (ACMG) secondary findings and recessive disease genes in different ethnic groups.ResultsIn the 56 ACMG secondary findings genes, the average number of predicted deleterious variants per individual was 0.74, and the mean number of ClinVar reported pathogenic variants was 0.06. We observed an average of 10 deleterious and 0.78 ClinVar reported pathogenic variants per individual in 1423 autosomal recessive disease genes. By repeatedly sampling pairs of exomes, 0.5 % of the randomly generated couples were at 25 % risk of having an affected offspring for an autosomal recessive disorder based on the ClinVar variants.ConclusionsBy investigating reported pathogenic and novel, predicted deleterious variants we estimated the lower and upper limits of the population fraction for which exome sequencing may reveal additional medically relevant information. We suggest that the observed wide range for the lower and upper limits of these frequency numbers will be gradually reduced due to improvement in classification databases and prediction algorithms.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0171-1) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Secondary findings and carrier test frequencies in a large multiethnic sample

et al. 2015

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“…While the current programs provide positive predictive power, their results are often in disagreement with each other, 2,3 and there are currently no guidelines as to which predictions are the most reliable. It is believed that individual methods have complementary strengths, depending on their specific features and computational algo-rithms.…”

Section: Introductionmentioning

confidence: 99%

ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants

Alirezaie

Kernohan

Hartley

et al. 2018

The American Journal of Human Genetics

181

196

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Advances in high-throughput DNA sequencing have revolutionized the discovery of variants in the human genome; however, interpreting the phenotypic effects of those variants is still a challenge. While several computational approaches to predict variant impact are available, their accuracy is limited and further improvement is needed. Here, we introduce ClinPred, an efficient tool for identifying disease-relevant nonsynonymous variants. Our predictor incorporates two machine learning algorithms that use existing pathogenicity scores and, notably, benefits from inclusion of normal population allele frequency from the gnomAD database as an input feature. Another major strength of our approach is the use of ClinVar-a rapidly growing database that allows selection of confidently annotated disease-causing variants-as a training set. Compared to other methods, ClinPred showed superior accuracy for predicting pathogenicity, achieving the highest area under the curve (AUC) score and increasing both the specificity and sensitivity in different test datasets. It also obtained the best performance according to various other metrics. Moreover, ClinPred performance remained robust with respect to disease type (cancer or rare disease) and mechanism (gain or loss of function). Importantly, we observed that adding allele frequency as a predictive feature-as opposed to setting fixed allele frequency cutoffs-boosts the performance of prediction. We provide pre-computed ClinPred scores for all possible human missense variants in the exome to facilitate its use by the community.

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“…() developed KinMut‐2, their tool for the interpretation of kinase variants, using a disease set constituted by 1,021 variants from 84 kinases. Several other studies have followed the same approach to build protein‐specific tools [Santibáñez‐Koref et al., ; Karchin et al., ; Torkamani and Schork ; Jordan et al., ; Stead et al., ; Crockett et al., ; Izarzugaza et al., ; Hamasaki‐Katagiri et al., ; Fechter and Porollo, ; Li et al., ; Leong et al., ; Masica et al., ; Niroula and Vihinen, ; Adebali et al., ]. In general, these articles coincide in that the protein‐specific (also referred to as gene‐specific in some works) approach gives a prediction performance equal to or better than that of general methods (GM), such as PolyPhen‐2 [Adzhubei et al., ] or SIFT [Kumar et al., ].…”

Section: Introductionmentioning

confidence: 99%

The Complementarity Between Protein-Specific and General Pathogenicity Predictors for Amino Acid Substitutions

2016

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The usage of next-generation sequencing with biomedical/clinical purposes has fuelled the demand for tools that assess the functional impact of sequence variants. For single amino acid variants, general methods (GM), based on biophysics/evolutionary principles and trained by pooling variants from many proteins, are already available. Until now, their accuracy range (∼80%) has limited their usage in clinical applications. In parallel, a series of studies indicate that protein-specific predictors (PSP), using only information from the protein of interest, could frequently surpass the performance of GM. However, two reasons suggest that this may not always be the case: the existence of a performance threshold affecting both GM and PSP, and the effect of training data scarcity. Here, we characterize the relationship between the two approaches deriving 82 PSP and comparing them with several GM (PolyPhen-2, SIFT, PON-P2, MutationTaster2, CADD). We find a complementary relationship between PSP and GM, with no approach always outperforming the other. However, the relationship varies between two limiting situations, for example, PSP are frequently outperformed by PON-P2, the best GM; however, the opposite happens when we compare PSP and SIFT. Finally, we explore how the observed complementarity could lead to increased success rates in pathogenicity prediction.

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Gene-Specific Function Prediction for Non-Synonymous Mutations in Monogenic Diabetes Genes

Cited by 26 publications

References 53 publications

Secondary findings and carrier test frequencies in a large multiethnic sample

Secondary findings and carrier test frequencies in a large multiethnic sample

ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants

The Complementarity Between Protein-Specific and General Pathogenicity Predictors for Amino Acid Substitutions

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